100-machine-learningdata_science_job.csv

day1.Hadding missing value by the help of CCA(complete-case analysis) day2. working with date and time data day3.Handling missing data using Mean , median ,Arbitary value

Name		Name	Last commit message	Last commit date
Latest commit History 119 Commits
day 55 regularized linear models		day 55 regularized linear models
day 15		day 15
day 17		day 17
day 19		day 19
day 20 univariate data analysis		day 20 univariate data analysis
day 21 bivariate data analysis		day 21 bivariate data analysis
day 22-pandas profiling		day 22-pandas profiling
day 24 standandrization		day 24 standandrization
day 25 normalization		day 25 normalization
day 26 ordinal Encoding		day 26 ordinal Encoding
day 27 one hot Encoding		day 27 one hot Encoding
day 28 column transformer		day 28 column transformer
day 29 sklearn pipelines		day 29 sklearn pipelines
day 30 function transformer		day 30 function transformer
day 31 power transformer		day 31 power transformer
day 32 binning and binarization		day 32 binning and binarization
day 33 handling mixed variable		day 33 handling mixed variable
day 34 handling time and date		day 34 handling time and date
day 35 case study		day 35 case study
day 36 mean-median -mode		day 36 mean-median -mode
day 37 handling missing categorical data		day 37 handling missing categorical data
day 38 missing indicator		day 38 missing indicator
day 39 knn imputer		day 39 knn imputer
day 40 iterative imputer		day 40 iterative imputer
day 42 outlier removal using zscore		day 42 outlier removal using zscore
day 43 outlier removal using iqr-method		day 43 outlier removal using iqr-method
day 44 outlier detection using percetiles		day 44 outlier detection using percetiles
day 45 feature construction and feature splitting		day 45 feature construction and feature splitting
day 47 pca		day 47 pca
day 48 simple linear regression		day 48 simple linear regression
day 49 regression metrics		day 49 regression metrics
day 50 multiple linear regression		day 50 multiple linear regression
day 51 gradient descent		day 51 gradient descent
day 52 gradient descent type		day 52 gradient descent type
day 53 polynomial regression		day 53 polynomial regression
day 57 elastic net regression		day 57 elastic net regression
day 58 logistic regression		day 58 logistic regression
day 59 classification-metrics		day 59 classification-metrics
day 60 polynomial logistic reggression		day 60 polynomial logistic reggression
day 65 random forest		day 65 random forest
day 66 adaboost -hyperparameter		day 66 adaboost -hyperparameter
day 68stacking and blending		day 68stacking and blending
day16		day16
day18		day18
day56 lasso regression		day56 lasso regression
kmeans		kmeans
templates		templates
05162021 Problem Statement.docx		05162021 Problem Statement.docx
30aug.ipynb		30aug.ipynb
Artificial Intelligence.docx		Artificial Intelligence.docx
Atmbasic		Atmbasic
CIS666 Artificial intelligence detecting lanes on highways.docx		CIS666 Artificial intelligence detecting lanes on highways.docx
Credit Card Customer Data.xlsx		Credit Card Customer Data.xlsx
Customer-Segmentation for Bank (1).ipynb		Customer-Segmentation for Bank (1).ipynb
Customer-Segmentation for Bank.ipynb		Customer-Segmentation for Bank.ipynb
Detecting Lanes on Highway.ipynb		Detecting Lanes on Highway.ipynb
README.md		README.md
Segmentation using Clustering (1).ipynb		Segmentation using Clustering (1).ipynb
Segmentation using Clustering.ipynb		Segmentation using Clustering.ipynb
arbitrary-value-imputation.ipynb		arbitrary-value-imputation.ipynb
code-from-scratch.ipynb		code-from-scratch.ipynb
data_science_job.csv		data_science_job.csv
day 21		day 21
day 27 one hot encoding		day 27 one hot encoding
day35.ipynb		day35.ipynb
hinge-loss-perceptron.ipynb		hinge-loss-perceptron.ipynb
lasso-regression-demo.ipynb		lasso-regression-demo.ipynb
mean		mean
mean-median-imputation.ipynb		mean-median-imputation.ipynb
messages.csv		messages.csv
oopsday1.5.ipynb		oopsday1.5.ipynb
orders.csv		orders.csv
polynomial-regression.ipynb		polynomial-regression.ipynb
ridge-regression-from-scratch.ipynb		ridge-regression-from-scratch.ipynb
titanic_toy.csv		titanic_toy.csv
working-with-dates-and-time.ipynb		working-with-dates-and-time.ipynb

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